A novel function of claudin-5 in maintaining the structural integrity of the heart and its implications in cardiac pathology.

This study aims to investigate the role of claudin-5 (Cldn5) in cardiac structural integrity. Proteomic analysis was performed to screen the protein profiles in enlarged left atrium from atrial fibrillation (AF) patients. Cldn5 shRNA adeno-associated virus (AAV) or siRNA was injected into the mouse left ventricle or added into HL1 cells respectively to knockdown Cldn5 in cardiomyocytes to observe whether the change of Cldn5 influences cardiac morphology and function, and affects those protein expressions stem from the proteomic analysis. Mitochondrial density and membrane potential were also measured by Mitotracker staining and JC-1 staining under the confocal microscope in HL1 cells. Cldn5 was reduced in cardiomyocytes from the left atrial appendage of AF patients compared to non-AF donors. Proteomic analysis showed 83 proteins were less abundant and 102 proteins were more abundant in AF patients. KEGG pathway analysis showed less abundant CACNA2D2, CACNB2, MYL2 and MAP6 were highly associated with dilated cardiomyopathy. Cldn5 shRNA AAV injection caused severe cardiac atrophy, dilation and myocardial dysfunction in mice. The decreases in mitochondrial numbers and mitochondrial membrane potentials in HL1 cells were observed after Cldn5 knockdown. We demonstrated for the first time the mechanism of Cldn5 downregulation-induced myocyte atrophy and myocardial dysfunction might be associated with the downregulation of CACNA2D2, CACNB2, MYL2 and MAP6, and mitochondrial dysfunction in cardiomyocytes.

A Single-Arm Multi-Center Phase II Clinical Trial of Cadonilimab (anti-PD-1/CTLA-4) in Combination with or without Conventional Second-Line Treatment for Patients with Extensive Stage Small Cell Lung Cancer.

BACKGROUND: Cadonilimab (AK104) is a bispecific IgG-single-chain Fv fragment (ScFv) antibody that binds to PD-1 and CTLA-4. Cadonilimab has shown encouraging anti-tumour activity and a favourable safety profile in several tumour types. In second-line treatment, there is no defined standard of care for patients with extensive-stage small-cell lung cancer (ES-SCLC). Cadonilimab is expected to show substantial clinical efficacy. OBJECTIVE: To assess the antitumor activity and safety of cadonilimab monotherapy or combination with conventional therapy in ES-SCLC patients who failed first-line treatment. METHODS: In this multicenter, open-label, phase II study, ES-SCLC patients who had failed first-line treatment, also aged 18 years to 70 years with histologically or cytologically confirmed ES-SCLC, and an Eastern Cooperative Oncology Group performance status (ECOG-PS) of 0-2 were eligible. Patients will receive cadonilimab 10 mg/kg every three weeks (Q3 W) among 24 months until progressive disease (PD) or adverse events (AE) discovery. The primary endpoint is progression-free survival (PFS). TRIAL REGISTRATION: NCT05901584.

TCCIA: a comprehensive resource for exploring CircRNA in cancer immunotherapy.

BACKGROUND: Immunotherapies targeting immune checkpoints have gained increasing attention in cancer treatment, emphasizing the need for predictive biomarkers. Circular RNAs (circRNAs) have emerged as critical regulators of tumor immunity, particularly in the PD-1/PD-L1 pathway, and have shown potential in predicting immunotherapy efficacy. Yet, the detailed roles of circRNAs in cancer immunotherapy are not fully understood. While existing databases focus on either circRNA profiles or immunotherapy cohorts, there is currently no platform that enables the exploration of the intricate interplay between circRNAs and anti-tumor immunotherapy. A comprehensive resource combining circRNA profiles, immunotherapy responses, and clinical outcomes is essential to advance our understanding of circRNA-mediated tumor-immune interactions and to develop effective biomarkers. METHODS: To address these gaps, we constructed The Cancer CircRNA Immunome Atlas (TCCIA), the first database that combines circRNA profiles, immunotherapy response data, and clinical outcomes across multicancer types. The construction of TCCIA involved applying standardized preprocessing to the raw sequencing FASTQ files, characterizing circRNA profiles using an ensemble approach based on four established circRNA detection tools, analyzing tumor immunophenotypes, and compiling immunotherapy response data from diverse cohorts treated with immune checkpoint blockades (ICBs). RESULTS: TCCIA encompasses over 4,000 clinical samples obtained from 25 cohorts treated with ICBs along with other treatment modalities. The database provides researchers and clinicians with a cloud-based platform that enables interactive exploration of circRNA data in the context of ICB. The platform offers a range of analytical tools, including browse of identified circRNAs, visualization of circRNA abundance and correlation, association analysis between circRNAs and clinical variables, assessment of the tumor immune microenvironment, exploration of tumor molecular signatures, evaluation of treatment response or prognosis, and identification of altered circRNAs in immunotherapy-sensitive and resistant tumors. To illustrate the utility of TCCIA, we showcase two examples, including circTMTC3 and circMGA, by employing analysis of large-scale melanoma and bladder cancer cohorts, which unveil distinct impacts and clinical implications of different circRNA expression in cancer immunotherapy. CONCLUSIONS: TCCIA represents a significant advancement over existing resources, providing a comprehensive platform to investigate the role of circRNAs in immuno-oncology.

Microfluidic Fabrication of Monodisperse and Recyclable TiO2-Poly(ethylene glycol) Diacrylate Hybrid Microgels for Removal of Methylene Blue from Aqueous Medium.

Nearly monodisperse titanium oxide-polyethylene glycol diacrylate [TiO2-P(EGDA)] hybrid microbeads containing 0.5 wt % TiO2 nanoparticles entrapped within a P(EGDA) cross-linked polymeric network were synthesized using a modular Lego-inspired glass capillary microfluidic device. TiO2-P(EGDA) hybrid microgels were characterized by optical microscopy, scanning electron microscopy, X-ray diffraction, energy dispersive X-ray spectroscopy, and thermogravimetric analysis. The fabricated TiO2-P(EGDA) hybrid microgel system showed 100% removal efficiency of methylene blue (MB) from its 1-3 ppm aqueous solutions after 4 h of UV light irradiation at 0.2 mW/cm2 at the loading of 25 g/L photocatalyst beads in the reaction mixture, corresponding to the loading of naked TiO2 of just 0.025 g/L. No decrease in photocatalytic efficiency was observed in 10 repeated runs with recycled photocatalyst using a fresh 1 ppm MB solution in each cycle. The rate of photocatalytic degradation was controlled by the UV light irradiance, catalyst loading, and the initial dye concentration. Physical adsorption of MB onto the surface of composite microgel was also observed. The adsorption data was best fitted with the Langmuir adsorption isotherm and the Elovich kinetic model. TiO2-P(EGDA) microgel beads are biocompatible, can be prepared with a tunable size in the microfluidic device, and can easily be separated from the reaction mixture by gravity settling. The TiO2-P(EGDA) system can be used for the removal of other toxic dyes and micropollutants from industrial wastewater.

Cancer genomics predicts disease relapse and therapeutic response to neoadjuvant chemotherapy of hormone sensitive breast cancers.

Several studies provide insight into the landscape of breast cancer genomics with the genomic characterization of tumors offering exceptional opportunities in defining therapies tailored to the patient's specific need. However, translating genomic data into personalized treatment regimens has been hampered partly due to uncertainties in deviating from guideline based clinical protocols. Here we report a genomic approach to predict favorable outcome to treatment responses thus enabling personalized medicine in the selection of specific treatment regimens. The genomic data were divided into a training set of N = 835 cases and a validation set consisting of 1315 hormone sensitive, 634 triple negative breast cancer (TNBC) and 1365 breast cancer patients with information on neoadjuvant chemotherapy responses. Patients were selected by the following criteria: estrogen receptor (ER) status, lymph node invasion, recurrence free survival. The k-means classification algorithm delineated clusters with low- and high- expression of genes related to recurrence of disease; a multivariate Cox's proportional hazard model defined recurrence risk for disease. Classifier genes were validated by Immunohistochemistry (IHC) using tissue microarray sections containing both normal and cancerous tissues and by evaluating findings deposited in the human protein atlas repository. Based on the leave-on-out cross validation procedure of 4 independent data sets we identified 51-genes associated with disease relapse and selected 10, i.e. TOP2A, AURKA, CKS2, CCNB2, CDK1 SLC19A1, E2F8, E2F1, PRC1, KIF11 for in depth validation. Expression of the mechanistically linked disease regulated genes significantly correlated with recurrence free survival among ER-positive and triple negative breast cancer patients and was independent of age, tumor size, histological grade and node status. Importantly, the classifier genes predicted pathological complete responses to neoadjuvant chemotherapy (P < 0.001) with high expression of these genes being associated with an improved therapeutic response toward two different anthracycline-taxane regimens; thus, highlighting the prospective for precision medicine. Our study demonstrates the potential of classifier genes to predict risk for disease relapse and treatment response to chemotherapies. The classifier genes enable rational selection of patients who benefit best from a given chemotherapy thus providing the best possible care. The findings encourage independent clinical validation.

The landscape of hepatobiliary adverse reactions across 53 herbal and dietary supplements reveals immune-mediated injury as a common cause of hepatitis.

Recent evidence suggests herbal-induced liver injury (HILI) to account for 20% of cases among the U.S. Drug-Induced-Liver-Injury-Network. To define injury patterns of HILI, we reviewed the clinical data of 413 patients exposed to 53 HDS products by considering the evidence for HILI and its grades of severity. Outstandingly, females developed HILI more rapidly (p = 0.018) and the time to recovery was significantly increased (p = 0.0153). > 90% of reported cases were severe and half of HDS products caused acute liver failure (ALF) requiring liver transplantation or resulted in fatal outcomes. Liver biopsies of 243 patients defined 13 histological features; two-thirds of products elicited immune-mediated hepatitis and included 154 Hy's law positive cases. The histological injury patterns were confirmed among unrelated patients, while accidental re-challenges evidenced culprits as causative. Furthermore, one-fifth of patients presented elevated autoantibody titres indicative of autoimmune-like HILI, and one-third of the products were linked to chronic hepatitis and cholestatic injuries not resolving within 6 months. Lastly, INR and TBL are critical laboratory parameters to predict progression of severe HILI to ALF. Our study highlights the need for a regulatory framework to minimize the risk for HILI. Better education of the public and a physician-supervised self-medication plan will be important measures to abate risk of HILI.

MicroRNA-138 negatively regulates the hypoxia-inducible factor 1α to suppress melanoma growth and metastasis.

Melanoma with rapid progression towards metastasis has become the deadliest form of skin cancer. However, the mechanism of melanoma growth and metastasis is still unclear. Here, we found that miRNA-138 was lowly expressed and hypoxia-inducible factor 1α (HIF1α) was highly expressed in patients' melanoma tissue compared with the paracancerous tissues, and they had a significant negative correlation (r=-0.877, P<0.001). Patients with miRNA-138low/HIF1αhigh signatures were predominant in late stage III/IV of melanoma. Further, bioinformatic analysis demonstrated that miRNA-138 directly targeted HIF1α. We found that the introduction of pre-miRNA-138 sequences to A375 cells reduced HIF1α mRNA expression and suppressed cell proliferation, migration and invasion. Overexpression of miRNA-138 or inhibition of HIF1α significantly suppressed the growth and metastasis of melanoma in vivo Our study demonstrates the role and clinical relevance of miRNA-138 and HIF1α in melanoma cell growth and metastasis, providing a novel therapeutic target for suppression of melanoma growth and metastasis.

Target-specific toxicity knowledgebase (TsTKb): a novel toolkit for in silico predictive toxicology.

As the number of man-made chemicals increases at an unprecedented pace, efforts of quickly screening and accurately evaluating their potential adverse biological effects have been hampered by prohibitively high costs of in vivo/vitro toxicity testing. While it is unrealistic and unnecessary to test every uncharacterized chemical, it remains a major challenge to develop alternative in silico tools with high reliability and precision in toxicity prediction. To address this urgent need, we have developed a novel mode-of-action-guided, molecular modeling-based, and machine learning-enabled modeling approach for in silico chemical toxicity prediction. Here we introduce the core element of this approach, Target-specific Toxicity Knowledgebase (TsTKb), which consists of two main components: Chemical Mode of Action (ChemMoA) database and a suite of prediction model libraries.

Long non-coding RNA MT1DP shunts the cellular defense to cytotoxicity through crosstalk with MT1H and RhoC in cadmium stress.

Metallothioneins (MTs) are known to protect cells against oxidative stress, especially providing protection against cadmium (Cd) toxicity in hepatocytes. There are various gene variants and pseudogenes for MTs; however, there is little understanding on the functions of those non-coding MT members that are known to be expressed as long non-coding RNAs (lncRNAs) nowadays. Different from most protein-coding MT members, MT1DP was here found that remarkably induced to provoke cytotoxicity in hepatocytes in response to Cd treatment. MT1DP exerted such a pro-apoptotic function in Cd-treated hepatocytes through interacting with two partners: RhoC and MT1H. On one hand, MT1DP interacted with RhoC protein to increase the latter's stability by preventing lysosome-dependent protein degradation. Therefore, upon Cd stress, MT1DP/RhoC complex was quickly reinforced to activate RhoC-CCN1/2-AKT signaling and potentiate Ca2+ influx, leading to enhanced Cd uptake and elevated Cd toxicity. On the other hand, MT1H, a protein-coding member of the MT family with little known function, was found to quickly respond to Cd exposure along with MT1DP. Mechanistically, MT1H and MT1DP were uncovered to mutually protect each other through a reciprocal ceRNA mechanism, building up a positive feedback loop to enforce MT1DP-conducted signaling upon Cd exposure. Moreover, MT1DP was found to contribute much more to the activation of RhoC-CCN1/2-AKT signaling than MT1H. Considered together, we here unveiled a mystery whether a pseudogene within the MT family, MT1DP, has actual biological functions in regulating Cd-induced cellular defense. Our findings unearthed an important role of pseudogene MT1DP in calibrating the cellular machinery to switch the cellular defense to cytotoxicity through crosslinking an interplay between its two partners, namely MT1H and RhoC, under cadmium stress.

Regulation and mechanism of miR-146 on renal ischemia reperfusion injury.

AIM: MicroRNAs (miRs) are endogenous substances that act as important diagnostic and treatment targets in renal diseases. miR-146 plays an important role in the development of endotoxin tolerance through NF-κB pathway, but the underlying mechanism is not clearly understood. The aim of this study was to determine the molecular regulation and function of miR-146 and also the expression of miR-146 in an experimental model of renal ischemia reperfusion injury (IRI). METHODS: IRI was induced in mouse by bilateral IRI for 45 min followed by reperfusion. The male mice were randomized as: sham, I/R, I/R+miR-146, and I/R+antago-miR-146 groups. Renal function, histological damage, and cell apoptosis were evaluated at 24 h after reperfusion. RESULTS: Overexpression of miR-146 protected renal function. Renal cells with upregulated miR-146 had lower plasma levels of blood urea nitrogen (BUN) and creatinine, decreased apoptosis and active caspase-3 protein expressions. miR-146 was shown to have a role in renal IR injury. miR-146 has a protective effect on renal function and plays a significant role in apoptosis. IGSF1 acts as a target of miR-146. IGSF1 rescued the effects of miR-146 on renal IRI. miR-146 protected renal function by activation of PI3K/AKT. CONCLUSION: These findings suggest that miR-146 might regulate apoptosis and can cause injury in I/R via targeting IGSF1 and also exert renal protection property.

A review on machine learning methods for in silico toxicity prediction.

In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have been employed in predicting the toxicity profile of chemicals. This review provides a detailed end-to-end overview of the application of machine learning algorithms to Structure-Activity Relationship (SAR)-based predictive toxicology. From raw data to model validation, the importance of data quality is stressed as it greatly affects the predictive power of derived models. Commonly overlooked challenges such as data imbalance, activity cliff, model evaluation, and definition of applicability domain are highlighted, and plausible solutions for alleviating these challenges are discussed.

PM2.5 exposure stimulates COX-2-mediated excitatory synaptic transmission via ROS-NF-κB pathway.

Long-term exposure to fine particulate matter (PM2.5) has been reported to be closely associated with the neuroinflammation and synaptic dysfunction, but the mechanisms underlying the process remain unclear. Cyclooxygenase-2 (COX-2) is a key player in neuroinflammation, and has been also implicated in the glutamatergic excitotoxicity and synaptic plasticity. Thus, we hypothesized that COX-2 was involved in PM2.5-promoted neuroinflammation and synaptic dysfunction. Our results revealed that PM2.5 elevated COX-2 expression in primary cultured hippocampal neurons and increased the amplitude of field excitatory postsynaptic potentials (fEPSPs) in hippocampal brain slices. And the administration of NS398 (a COX-2 inhibitor) prevented the increased fEPSPs. PM2.5 also induced intracellular reactive oxygen species (ROS) generation accompanied with glutathione (GSH) depletion and the loss of mitochondrial membrane potential (MMP), and the ROS inhibitor, N-acetyl-L-cystein (NAC) suppressed the COX-2 overexpression and the increased fEPSPs. Furthermore, the nuclear factor kappa B (NF-κB) was involved in ROS-induced COX-2 and fEPSP in response to PM2.5 exposure. These findings indicated that PM2.5 activated COX-2 expression and enhanced the synaptic transmission through ROS-NF-κB pathway, and provided possible biomarkers and specific interventions for PM2.5-induced neurological damage.

The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury.

INTRODUCTION: Drug-induced liver injury (DILI) is challenging for drug development, clinical practice and regulation. The Liver Toxicity Knowledge Base (LTKB) provides essential data for DILI study. Areas covered: The LTKB provided various types of data that can be used to assess and predict DILI. Among much information available, several reference drug lists with annotated human DILI risk are of important. The LTKB DILI classification data include DILI severity concern determined by the FDA drug labeling, DILI severity score from the NIH LiverTox database, and other DILI classification schemes from the literature. Overall, ~1000 drugs were annotated with at least one classification scheme, of which around 750 drugs were flagged for some degree of DILI risk. Expert commentary: The LTKB provides a centralized repository of information for DILI study and predictive model development. The DILI classification data in LTKB could be a useful resource for developing biomarkers, predictive models and assessing data from emerging technologies such as in silico, high-throughput and high-content screening methodologies. In coming years, streamlining the prediction process by including DILI predictive models for both DILI severity and types in LTKB would enhance the identification of compounds with the DILI potential earlier in drug development and risk assessment.

Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) is complex in mechanism. Different drugs could undergo different mechanisms but result in the same DILI type, while the same drug could lead to different DILI types via different mechanisms. Therefore, predicting a drug's potential for DILI should take its underlying mechanisms into consideration. To achieve that, we constructed a novel approach by incorporating the drug's Mode of Action (MOA) into Quantitative Structure-Activity Relationship (QSAR) modeling. This MOA-DILI approach was examined using a data set of 333 drugs. The drugs were first grouped according to their MOA profiles (positive or negative in each MOA) based on the Tox21 qHTS assays. QSAR models for individual MOA assays were developed and subsequently combined to obtain the MOA-DILI model. A hold-out testing strategy (222 drugs for training and 111 drugs as a test set) was employed, which yielded a predictive accuracy of 0.711. The MOA-DILI model was directly compared with the standard QSAR approach using the same hold-out strategy, and the QSAR model yielded an accuracy of 0.662. To minimize the random chance in splitting training/test sets, the hold-out testing process was repeated 1000 times, and the observed difference in prediction accuracy between MOA-DILI and QSARs was statistically significant (P value <0.0001). Out of 17 MOAs used, four assays (i.e., antioxidant response elements, PPAR-gamma, estrogen receptor, and thyroid receptor assays) contributed most to the improved prediction of the MOA-DILI model over QSARs. In conclusion, the MOA-DILI approach has the potential to significantly improve predictive outcomes and to reveal complex relationships between MOAs and DILI, all of which would be helpful in developing DILI predictive models in drug screening and for risk assessment of industrial chemicals.

Nrf-2-driven long noncoding RNA ODRUL contributes to modulating silver nanoparticle-induced effects on erythroid cells.

The biosafety and biological effects of silver nanoparticles (AgNPs) on human health attract increasing concern. Although considerable studies have been performed to reveal the molecular mechanisms responsible for AgNP-induced effects, the current understanding mainly focuses on oxidative stress-associated signaling pathways activated by Ag particles and/or Ag ions. However, the molecular bases underlying the activation of these stress signaling pathways have not been thoroughly elucidated yet. In the current study, we aimed to shed light on the molecular bases of AgNP-induced effects on erythroid cells from the perspective of long noncoding RNAs. We identified a long-noncoding RNA molecule, ODRUL, which was substantially enhanced in K562 erythroid cells responding to AgNPs, coupled to accelerated cell death. Further, we uncovered oxidative stress-driven Nrf2 transcriptionally promoted ODRUL expression in K562 cells. Downstream of Nrf2-ODRUL activation by AgNPs, ODRUL was recognized to interact with PI4Kα protein to modulate the activities of its targets AKT and JNK. As a result, the Bcl-2 level was negatively regulated by PI4K-AKT/JNK signaling under AgNP-induced stress, leading to enhanced cell death. Together, our findings unearthed that Nrf2-mediated lncRNA ODRUL was indispensable for AgNP-induced toxicity in erythroid cells through regulation of AKT/JNK-Bcl-2 signaling dependent on a physical interaction with PI4Kα. Thus, this study would open a new path to depict the molecular bases of AgNP-induced effects on erythroid cells.

Endocannabinoid 2-arachidonoylglycerol protects inflammatory insults from sulfur dioxide inhalation via cannabinoid receptors in the brain.

Sulfur dioxide (SO2) pollution in the atmospheric environment causes brain inflammatory insult and inflammatory-related microvasculature dysfunction. However, there are currently no effective medications targeting the harmful outcomes from chemical inhalation. Endocannabinoids (eCBs) are involved in neuronal protection against inflammation-induced neuronal injury. The 2-arachidonoylglycerol (2-AG), the most abundant eCBs and a full agonist for cannabinoid receptors (CB1 and CB2), is also capable of suppressing proinflammatory stimuli and improving microvasculature dysfunction. Here, we indicated that endogenous 2-AG protected against neuroinflammation in response to SO2 inhalation by inhibiting the activation of microglia and astrocytes and attenuating the overexpression of inflammatory cytokines, including tumor necrosis factor alpha (TNF-a), interleukin (IL)-1ß, and inducible nitric oxide synthase (iNOS). In addition, endogenous 2-AG prevented cerebral vasculature dysfunction following SO2 inhalation by inhibiting endothelin 1 (ET-1), vascular cell adhesion molecule-1 (VCAM-1) and intercellular adhesion molecule 1 (ICAM-1) expression, elevating endothelial nitric oxide synthase (eNOS) level, and restoring the imbalance between thromboxane A2 (TXA2) and prostaglandin I2 (PGI2). In addition, the action of endogenous 2-AG on the suppression of inflammatory insult and inflammatory-related microvasculature dysfunction appeared to be mainly mediated by CB1 and CB2 receptors. Our results provided a mechanistic basis for the development of new therapeutic approaches for protecting brain injuries from SO2 inhalation.

NETBAGs: a network-based clustering approach with gene signatures for cancer subtyping analysis.

AIM: To evaluate gene signature and network-based approach for cancer subtyping and classification. MATERIALS & METHODS: Here we introduced NETwork Based clustering Approach with Gene signatures (NETBAGs) algorithm, which clustered samples based on gene signatures and identified molecular markers based on their significantly expressed gene network profiles. RESULTS: Applying NETBAGs to multiple independent breast cancer datasets, we demonstrated that the clustering results were highly associated with the clinical subtypes and clearly revealed the genomic diversity of breast cancer samples. CONCLUSION: NETBAGs algorithm is able to classify samples by their genomic signatures into clinically significant phenotypes so that potential biomarkers can be identified. The approach may contribute to cancer research and clinical study of complex diseases.

A unifying ontology to integrate histological and clinical observations for drug-induced liver injury.

Drug-induced liver injury (DILI) may present any morphologic characteristic of acute or chronic liver disease with no standardized terminology in place. Defining lexemes of DILI histopathology would allow the development of advanced knowledge discovery and data mining tools for across comparisons of publicly available information. For these purposes, a DILI ontology (DILIo) was developed by using the Unified Medical Language System tool and the standardized terminology of the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT). The DILIo was entrained on findings of 114 US Food and Drug Administration-approved drugs by extracting all clinically DILI-related histopathologic descriptions for 1082 liver biopsy samples, which were then analyzed using the Unified Medical Language System MetaMap and subsequently mapped to the SNOMED CT. The DILIo provides a standard means to describe and organize liver injury induced by drugs, enabling comparative analysis of drugs within and across histopathologic terms. The analysis showed that flutamide, troglitazone, diclofenac, isoniazid, and tamoxifen were reported to have the most diverse histopathologic observations in liver biopsy. Necrosis, cholestasis, fatty degeneration, fibrosis, infiltrate, and hepatic necrosis were the most frequent terms used as descriptors of histopathologic features of DILI. In conclusion, DILIo entrains different algorithms for an efficient meta-analysis of published findings for an improved understanding of mechanisms and clinical characteristics of DILI.

atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

BACKGROUND: Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. RESULTS: atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. CONCLUSION: atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

Selecting a single model or combining multiple models for microarray-based classifier development?--a comparative analysis based on large and diverse datasets generated from the MAQC-II project.

BACKGROUND: Genomic biomarkers play an increasing role in both preclinical and clinical application. Development of genomic biomarkers with microarrays is an area of intensive investigation. However, despite sustained and continuing effort, developing microarray-based predictive models (i.e., genomics biomarkers) capable of reliable prediction for an observed or measured outcome (i.e., endpoint) of unknown samples in preclinical and clinical practice remains a considerable challenge. No straightforward guidelines exist for selecting a single model that will perform best when presented with unknown samples. In the second phase of the MicroArray Quality Control (MAQC-II) project, 36 analysis teams produced a large number of models for 13 preclinical and clinical endpoints. Before external validation was performed, each team nominated one model per endpoint (referred to here as 'nominated models') from which MAQC-II experts selected 13 'candidate models' to represent the best model for each endpoint. Both the nominated and candidate models from MAQC-II provide benchmarks to assess other methodologies for developing microarray-based predictive models. METHODS: We developed a simple ensemble method by taking a number of the top performing models from cross-validation and developing an ensemble model for each of the MAQC-II endpoints. We compared the ensemble models with both nominated and candidate models from MAQC-II using blinded external validation. RESULTS: For 10 of the 13 MAQC-II endpoints originally analyzed by the MAQC-II data analysis team from the National Center for Toxicological Research (NCTR), the ensemble models achieved equal or better predictive performance than the NCTR nominated models. Additionally, the ensemble models had performance comparable to the MAQC-II candidate models. Most ensemble models also had better performance than the nominated models generated by five other MAQC-II data analysis teams that analyzed all 13 endpoints. CONCLUSIONS: Our findings suggest that an ensemble method can often attain a higher average predictive performance in an external validation set than a corresponding "optimized" model method. Using an ensemble method to determine a final model is a potentially important supplement to the good modeling practices recommended by the MAQC-II project for developing microarray-based genomic biomarkers.